When calculating FID scores, samples are first initialized from the generator then updated with the EBM using a number of Langevin updates tuned to provide optimal synthesis quality. As mentioned before, an appealing aspect of EBM learning with generator initialization is the ease of generating new images from scratch, in contrast with persistent initialization. We use 50K samples with the official FID code from [1]} to calculate all FID scores. In particular, our scores and framework are consistent with the GAN replication library from [2]}. We publicly release the checkpoints, learning code, and FID code for each model. The checkpoints are representative of what is achievable within an ordinary run of the code provided. Our CIFAR-10 results show a significant improvement over prior EBM synthesis and contribute to closing the gap between EBMs and other generative models. Surprisingly, our ImageNet results surpass the results of GANs such as SNGAN [3]} and SSGAN [4]} on unconditional synthesis using a similar magnitude of computational resources. In Figure REF , we demonstrate cooperative and persistent initialization cannot effectively scale to ImageNet, and that our initialization method provides a dramatic improvement in synthesis quality.
